Forthcoming articles


International Journal of Business Intelligence and Systems Engineering


These articles have been peer-reviewed and accepted for publication in IJBISE, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


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International Journal of Business Intelligence and Systems Engineering (3 papers in press)


Regular Issues


    by Balasundaram Rathinam, Sathiya Devi S 
    Abstract: Recently, the instance selection is getting more attention for the researchers to achieve enhanced performance of the algorithm. A typical flowshop dataset can be represented in the form of a number of instances. The Instances that are recorded during production process may not be a good example to learn useful knowledge. Therefore, the selection of high quality instances can be considered as a search problem and solved by evolutionary algorithms. In this work, Genetic Algorithm (GA) is proposed to select sub-set of best instances. The selected instances (low level data) are represented in the form of IF-Then else rules (high level knowledge) using Decision Tree (DT) algorithm. Since, the DT is heuristics the seed solution generated from DT may not yield a good solution. Hence, seed solution from DT is given to input of Scatter Search (SS) algorithm for few iterations which acts as a local search to find the best value of the selected instances. The GA is used to select best instances in order to have a less tree size (number of rules are minimum) with good solution accuracy for minimizing makespan criterion in permutation flowshop scheduling. The computational experiments are performed with standard problems and compared against various existing literatures. Statistical tests of significance are performed to verify the development in solution.
    Keywords: Instance selection; Genetic algorithm; Decision Tree algorithm; Makespan;.

  • Refining Information Systems (IS) Competencies: The Role of Big Data Analytics Resilience (BDARISC) in Organizational Learning (OL)   Order a copy of this article
    by James Rodger, Pankaj Chaudhary, Ganesh Bhatt 
    Abstract: Information systems competencies (ISC) has been an important area of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business advantages, a firm must be able to renew its IS competencies. Although the literature has discussed the importance of organizational learning (OL), there is not much known about how organizational learning renews IS competencies. Less is known about Big Data Analytics Resilience Information System Competencies (BDARISC) and the organizations ability to recover from lost information. In this research, we take a step in this direction and analyze the relationship of organizational learning on IS competencies for business advantages and investigate IS infrastructure flexibility, expertise, trust relationships, business performance and Big Data Analytics Resilience. We collected data through a survey of IS and business managers from 302 participants. COMPUSTAT was used to collect business profitability data. Our SEM results show that while both models contribute to organizational learning, (BDARISC) Model outperforms the Big Data Analytics Resilience (BDAR) Model. The model presented here is unique because it incorporates BDARISC into the organizational learning model by incorporating the CD-RISC resilience model. We adapted the OL model to develop an instrument for obtaining manager evaluations of flexibility, expertise, trust, performance and big data analytics.
    Keywords: IS competencies; organizational knowledge; business advantages; Big Data Analytics; Resilience; flexibility; expertise; trust; business performance.

  • Understanding User Demographics Using Public Transportation Data   Order a copy of this article
    by Sherief Abdallah 
    Abstract: As smart cards become dominant in public transportation, more data is collected that capture passenger behaviors: their trip times, which stations do they hop in/out, etc. Understanding passenger demographics can have important applications for health and marketing, as well as public transportation. For example, if we know that a certain metro station has high concentration of yound children (students), then the transport authority may increase the security in the station to ensure the children safety. In this work, we collect real smart card data from a cosmopolitan city in the middle east and analyze how it relates to the underlying passenger demographics. Our analysis illustrates how association rule-mining can expose rules that are indicative of age, gender, and nationality based on usage patterns of public transportation.
    Keywords: Intelligent Transportation; Association Rule Mining; Passenger demographics.